Track Charge Loss based on Signal and Noise Characteristics of Memory Cells Collected in Calibration Operations

ABSTRACT

A memory sub-system to track charge loss in memory cells and shifts of voltages optimized to read the memory cells. For example, a memory device can measure signal and noise characteristics of a group of memory cells to calculate an optimized read voltage of the group of memory cells. The memory sub-system having the memory device can determine an amount of charge loss in the group of memory cells, using at least the signal and noise characteristics, the optimized read voltage, and/or the bit error rate of data read using the optimized read voltage. The memory sub-system tracks changes in optimized read voltages of memory cells in the memory device based on the amount of charge loss.

FIELD OF THE TECHNOLOGY

At least some embodiments disclosed herein relate to memory systems ingeneral, and more particularly, but not limited to memory systemsconfigured to determine charge loss based at least in part on signal andnoise characteristics of memory cells.

BACKGROUND

A memory sub-system can include one or more memory devices that storedata. The memory devices can be, for example, non-volatile memorydevices and volatile memory devices. In general, a host system canutilize a memory sub-system to store data at the memory devices and toretrieve data from the memory devices.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings in which like referencesindicate similar elements.

FIG. 1 illustrates an example computing system having a memorysub-system in accordance with some embodiments of the presentdisclosure.

FIG. 2 illustrates an integrated circuit memory device having acalibration circuit configured to measure signal and noisecharacteristics according to one embodiment.

FIG. 3 shows an example of measuring signal and noise characteristics toimprove memory operations according to one embodiment.

FIGS. 4-6 illustrate a technique to compute an optimized read voltagefrom count differences according to one embodiment.

FIG. 7 shows a technique to determine charge loss using signal and noisecharacteristics of memory cells collected during calibration of theoptimized read voltages of the memory cells according to one embodiment.

FIG. 8 shows a method to track charge loss according to one embodiment.

FIG. 9 is a block diagram of an example computer system in whichembodiments of the present disclosure can operate.

DETAILED DESCRIPTION

At least some aspects of the present disclosure are directed to a memorysub-system configured to track charge loss based on signal and noisecharacteristics measured during calibrations of read voltages of thegroup of memory cells. Examples of storage devices and memory modulesare described below in conjunction with FIG. 1. In general, a hostsystem can utilize a memory sub-system that includes one or morecomponents, such as memory devices that store data. The host system canprovide data to be stored at the memory sub-system and can request datato be retrieved from the memory sub-system.

An integrated circuit memory cell (e.g., a flash memory cell) can beprogrammed to store data by the way of its state at a threshold voltage.For example, if the memory cell is configured/programmed in a state thatallows a substantial current to pass the memory cell at the thresholdvoltage, the memory cell is storing a bit of one; and otherwise, thememory cell is storing a bit of zero. Further, a memory cell can storemultiple bits of data by being configured/programmed differently atmultiple threshold voltages. For example, the memory cell can storemultiple bits of data by having a combination of states at the multiplethreshold voltages; and different combinations of the states of thememory cell at the threshold voltages can be interpreted to representdifferent states of bits of data that is stored in the memory cell.

However, after the states of integrated circuit memory cells areconfigured/programmed using write operations to store data in the memorycells, the optimized threshold voltage for reading the memory cells canshift due to a number of factors, such as charge loss, read disturb,cross-temperature effect (e.g., write and read at different operatingtemperatures), etc., especially when a memory cell is programmed tostore multiple bits of data.

Data can be encoded with redundant information to facilitate errordetection and recovery. When data encoded with redundant information isstored in a memory sub-system, the memory sub-system can detect errorsin raw, encoded data retrieved from the memory sub-system and/or recoverthe original, non-encoded data that is used to generated encoded datafor storing in the memory sub-system. The recovery operation can besuccessful (or have a high probability of success) when the raw, encodeddata retrieved from the memory sub-system contains less than a thresholdamount of errors, or the bit error rate in the encoded data is lowerthan a threshold. For example, error detection and data recovery can beperformed using techniques such as Error Correction Code (ECC),Low-Density Parity-Check (LDPC) code, etc.

When the encoded data retrieved from the memory cells of the memorysub-system has too many errors for successful decoding, the memorysub-system may retry the execution of the read command with adjustedparameters for reading the memory cells. However, it is inefficient tosearch for a set of parameters through multiple read retry with multiplerounds of calibration, reading, decoding failure, and retry, until theencoded data retrieved from the memory cells can be decoded into errorfree data. For example, blind searching for the optimized read voltagesis inefficient. For example, one or more commands being injected betweenretry reads can lead to long latency for recovering data from errors.

Conventional calibration circuitry has been used to self-calibrate amemory region in applying read level signals to account for shift ofthreshold voltages of memory cells within the memory region. During thecalibration, the calibration circuitry is configured to apply differenttest signals to the memory region to count the numbers of memory cellsthat output a specified data state for the test signals. Based on thecounts, the calibration circuitry determines a read level offset valueas a response to a calibration command.

At least some aspects of the present disclosure address the above andother deficiencies by determining an amount of charge loss in a group ofmemory cells based at least in part on the signal and noisecharacteristics of memory cells measured during the calibration of theoptimized read voltages of the group of memory cells, and tracking theamount of charge loss to determine estimates of optimized read voltagesas a starting point in a subsequent calibration of the optimized readvoltages of the group of memory cells, or another group of memory cells.

Charge loss can cause the optimized read voltages of a memory cell toshift in a systematic way. In the absence of other factors that causeshifts or changes in the optimized read voltages of the memory cell, theamount of charge loss in a group of memory cells correlates with thehighest threshold voltage and the optimized read voltages at differentlevels for reading a memory cell that is programmed to store multiplebits data.

Based on the correlation between the highest threshold voltage andshifts in the optimized read voltages at different levels (e.g., causedby charge loss), a look up table can be configured to provide estimatesof optimized read voltages at the different levels for reading thememory cell based on an estimate of the range of the highest thresholdvoltage. The estimates of the optimized read voltages determined in sucha way can be used as the result of a coarse calibration of the readvoltages of the memory cell. A fine calibration can be performed tocapture further shifts in optimized read voltages that may be caused byother factors, such as read disturb, cross-temperature effect, etc.

The highest threshold voltage can be estimated during a read sensingoperation in a memory device (e.g., the dynamic voltage for controllingthe gate of memory cells during read, or dynamic VpassR). However, suchan estimate can be inaccurate in many circumstances. For example, when ablock is written over the course of a long period of time (e.g., anhour), different groups of memory cells can have different shifts intheir highest threshold voltages, since the shifts depend upon when theword line in the block is written. Thus, the memory device may not beable to accurately sense the highest threshold voltage of a group ofmemory cells.

In at least some embodiments disclosed herein, the amount of charge lossin a group of memory cells is computed or estimated using at least thesignal and noise characteristics measured during the fine calibration ofthe optimized read voltages. The amount of charge loss can be determinedby applying the measured signal and noise characteristics as input to apredictive model. The predictive model can further use additionalinputs, such as the optimized read voltage calibrated at the highestlevel of read voltages of the group of memory cells and/or optimizedread voltages at other levels, a count of bit errors identified in theresults of memory data that is retrieved from the group of memory cellsusing the optimized read voltages. Amounts of charge loss can be trackedto facilitate the determination of estimates of optimized read voltagesin coarse calibration.

For a given estimate of an optimized read voltage, a memory device canautomatically perform a fine calibration of the optimized read voltageby measuring signal and noise characteristics of a group of memorycells. The signal and noise characteristics measured for memory cellscan be based on a bit count of memory cells in the group having apredetermined status when a test voltage is applied to read the memorycells. Different test voltages near the given estimate of the optimizedread voltage and separated from one another by a predetermined voltageinterval or gap can have different bit counts. The difference betweenbit counts of two adjacent test voltages provides the count differencefor the voltage interval or gap between the adjacent test voltages. Anoptimized read voltage can be found at a voltage where the distributionof the count differences over voltage reaches a minimum.

To improve efficiency and accuracy, the range of the test voltages istypically configured to be small in comparison to the possible range ofthe optimized read voltage. To increase the chance that the optimizedread voltage to be determined is located within the test voltage range,the test voltage range for measuring the signal and noisecharacteristics in the determination of the fine calibration of theoptimized read voltage is configured to be centered at the givenestimate of the optimized read voltage (e.g., the coarse calibration ofthe optimized read voltage). Such a coarse calibration can bedetermined, for example, from the look up table based on the estimate ofthe highest threshold voltage.

A predictive model can be used to compute/estimate/predict the chargeloss in the group of memory cells based on the signal and noisecharacteristics of the group of memory cells. For example, the input tothe predictive model can include the count differences measured on thetest voltage ranges at the different read voltage levels, the optimizedread voltage(s) calculated from the count differences, and a count ofbit errors detected during the decoding of the memory data obtainedaccording to the optimized read voltages. The decoding can be performedusing an error detection and data recovery technique, such as ErrorCorrection Code (ECC), Low-Density Parity-Check (LDPC) code, etc.Optionally, the count of bit errors can also be estimated from the countdifferences so that the charge loss can be computed/estimated withoutreading the memory cells at the optimized read voltages to retrievememory data and/or without decoding the memory data retrieved using theoptimized read voltages.

The predictive model for the computing of the charge loss amount can beobtained using a machine learning technique, or another technique, suchas statistical analysis, correlation, classification tree, etc.

In one embodiment, the predictive model is configured to compute anestimated amount of charge loss from the count differences measured forthe highest read voltage (e.g., the read voltage at the highest level ofreading the group of memory cells), the highest optimized read voltage,and the count of bit errors detected during the decoding of the memorydata retrieved using the optimized read voltages. In other embodiments,the predictive model is configured to further use count differencesmeasured for other lower read voltage levels and/or the lower optimizedread voltages as part of the input to compute the estimated amount ofcharge loss.

An amount of charge loss can be used as, or mapped into, an index usablein a table to look up the estimated read voltage of a group of memorycells having the amount of charge loss. The estimated read voltage canbe at the highest read voltage level of the group of memory cells.Optionally, the table can also provide estimated read voltages at otherlower voltage levels for reading the group of memory cells. Theestimates obtained from the table according to the amount of charge losscan be used as a coarse calibration of the read voltages of the group ofmemory cells.

A memory sub-system 110 can store data identifying time pools of memorycells. Each time pool identifies a set of memory cell groups that havesimilar amounts of charge loss (e.g., within a threshold from a nominalamount of charge loss). Thus, the optimized read voltages of the timepool can be used as the coarse calibration of the optimized readvoltages of any group in the time pool.

In general, charge loss in a group of memory cells during a period oftime depends not only the length of the period but also the temperatureof the memory cells during the period of time. For a given amount ofcharge loss, the nominal length of a time period for the memory cells toaccumulate, under a predetermined, nominal condition (e.g.,temperature), the given amount of charge loss can be calculated. Such anominal length can be different from the actual time period that causesthe given amount of charge loss, because the actual temperature of thememory cells may be different from the temperature in the predetermined,nominal condition.

In one embodiment, the time pools are configured based on the nominaltime lengths of memory cells from their last write operations. Thus, thetime lengths tracked from the time pools are normalized according to thepredetermined, nominal condition (e.g., a predetermined temperature ofmemory cells).

The amount of charge loss determined from the predictive model accordingto the signal and characteristics of memory cells can be used todetermine a nominal time length for the amount of charge loss under thepredetermined, nominal condition. Thus, the time lengths of the timepools can be tracked accurately under the predetermined, nominalcondition, while the actual condition of the group of memory cell canvary from time to time.

For example, when a memory sub-system 110 is powered off for a period oftime, the actually time period during which the memory sub-system 110 ispowered off can be different from the nominal time length of charge lossunder the predetermined, nominal condition (e.g., a predeterminedtemperature). The actually time period along is insufficient for theestimate of charge loss during the time period, because the charge lossis also dependent on the temperature history of the memory cells duringthe time period. The actual temperature history of the memory cellsgenerally deviates from the predetermined temperature. Since the actualtemperature history is generally unknown, it is difficult to obtain anestimate of the charge loss based on the actual time length of the poweroff period. However, the amounts of charge loss in the group of memorycells before and after the power off period can be estimated from thesignal and noise characteristics of the group of memory cells before andafter the power off period. The difference between the amounts of chargeloss in the group of memory cells before and after the power off periodcan be used to identify the nominal time length of the additional amountof charge loss accumulated under the predetermined, nominal condition(e.g., a predetermined temperature) during the power off period. Thisnominal time length can be used to update the time pool that includesthe group, but also other time pools.

For example, after the memory sub-system 110 is powered on following thepower off period, the amount of charge loss determined from thepredictive model according to the signal and characteristics of memorycells can be used to determine a nominal time length for the amount ofcharge loss under the predetermined, nominal condition. This nominaltime length of the charge loss amount at the time of powering on can becompared to the corresponding nominal time length of the charge lossamount at the time of powering off. Thus, the nominal time duration ofcharge loss during the power off period can be determined and used toupdate the time lengths of the time pools. Optionally, such a nominaltime duration of charge loss during the power off period can be computedfor each of multiple memory cell groups; and an average of the nominaltime lengths of the groups can be used to update the time pools.

In general, the amounts of charge loss determined from the predictivemodel for a group of memory cells at two different time instances can beused to calculate the nominal time duration of charge loss between thetwo time instances. The nominal time duration can be used to update thetime pools of memory cells to track their charge loss.

In some instances, different groups of memory cells in the memorysub-system 110 can have different temperatures during operation. Asresult, the different groups can have different rates in accumulatingcharge loss. Such groups can be assigned to different time pools eventhrough data may be written into the memory cells approximately at thesame time. For example, after the amount of charge loss is determinedfrom the predictive model during the execution of a read or calibrationcommand for a group, the nominal time of writing the data into the groupto accumulate the amount of charge loss under the predetermined, nominalcondition can be calculated to assign the group to a time poolassociated with the nominal time of writing. Thus, the optimized readvoltages of the group of memory cells can be tracked in association withthe time pool.

After count differences are measured on a test voltage range, the memorydevice can calculate an optimized read voltage from the countdifferences in the test voltage range.

For example, when one of the count differences in the test voltage rangeis smaller than its two adjacent neighbors, the minimum corresponding tothe optimized read voltage can be considered to be in the voltageinterval or gap of the smallest count difference. An improved locationof the optimized read voltage within the gap can be computed based on aratio of adjacent neighbors, as further discussed below in connectionwith FIG. 5.

For example, when no count difference is between two higher adjacentneighbors in a test voltage range, the optimized read voltage can beidentified as in a voltage interval or gap corresponding to a countdifference that is smaller than two of the next two count differences.An improved location of the optimized read voltage within the gap can becomputed based on a ratio of bit counts at the test voltages of the twoends of the gap, as further discussed below in connection with FIG. 6.

After an optimized read voltage is calculated (e.g., using techniquesillustrated in FIGS. 3-6), the memory device can use the optimized readvoltage to read memory cells and obtain hard bit data, and optionallyboost modulating the applied read voltage(s) to adjacent voltages tofurther read the memory cells for soft bit data.

Preferably, the operations of reading the hard bit data and reading thesoft bit data are scheduled together during the execution of the readcommand to minimize the time required to obtain the soft bit data and/orto avoid delay that can be caused by processing a separate read command,or by intervening operations on the memory cells.

Optionally, the signal and noise characteristics measured for memorycells are further used to evaluate the quality of the hard bit dataretrieved using the calibrated read voltage(s). The evaluation can beperformed at least in part concurrently with the reading of the hard bitdata. Based on the evaluated quality of the hard bit data, the memorydevice may selectively read and/or transmit the soft bit data.

The hard bit data retrieved from a group of memory cells using thecalibrated/optimized read voltage can be decoded using an errordetection and data recovery technique, such as Error Correction Code(ECC), Low-Density Parity-Check (LDPC) code, etc. When the error rate inthe hard bit data is high, the soft bit data, retrieved from the memorycell using read voltages with predetermined offsets from thecalibrated/optimized read voltage, can be used to assist the decoding ofthe hard bit data. When the soft bit data is used, the error recoverycapability is improved in decoding the hard bit data.

Optionally, a controller of a memory sub-system can initially send acommand to a memory device to read hard bit data with calibrated readvoltage; and in response to a failure in the decoding of the hard bitdata, the controller can further send a command to the memory device toread the corresponding soft bit data. Such an implementation isefficient when the likelihood of a failure in decoding the hard bit datawithout soft bit data is lower than a threshold. However, when thelikelihood is above the threshold, the overhead of sending the separatecommand becomes disadvantageous.

When the likelihood of using soft bit data is above a threshold, it isadvantageous to transmit a single command to the memory device to causethe memory device to read the soft bit data and the hard bit datatogether. Further, the memory device can use the signal and noisecharacteristics of the memory cells to predict whether the soft bit datais likely to be used by the controller. If the likelihood of using ofthe soft bit data is lower than a threshold, the memory device can skipreading the soft bit data.

For example, during the calibration operation, the memory device canmeasure the signal and noise characteristics of the memory cells and usethe measurements to calculate an optimized/calibrated read voltage forreading the memory cells. Once the optimized/calibrated read voltage isobtained, the memory device reads the memory cells to obtain the hardbit data. Subsequently, the memory device adjusts the already appliedoptimized/calibrated read voltage (e.g., through boosted modulation) toa predetermined offset (e.g., 50 mV) below the optimized/calibrated readvoltage to retrieve a set of data, and further adjusts the currentlyapplied voltage (e.g., through boosted modulation) to the predeterminedoffset above the optimized/calibrated read voltage to retrieve anotherset of data. The logic operation of XOR (exclusive or) of the two setsof data at the both sides of the offset (e.g., 50 mV) from theoptimized/calibrated read voltage provides the indication of whether thememory cells provide the same reading at the offset locations around theoptimized/calibrated read voltage. The result of the XOR operation canbe used as soft bit data for decoding the hard bit data read using theoptimized/calibrated read voltage. In some implementations, a largeroffset (e.g., 90 mV) can be used to read another set of soft bit datathat indicates whether the memory cells provide the same reading at thelocations according to the larger offset (e.g., 90 mV) around theoptimized/calibrated read voltage.

For example, in response to a read command from a controller of thememory sub-system, a memory device of the memory sub-system performs anoperation to calibrate a read voltage of memory cells. The calibrationis performed by measuring signal and noise characteristics throughreading the memory cells at a number of voltage levels that are near anestimated location of the optimized read voltage. An optimized readvoltage can be calculated based on statistical data of the resultsgenerated from reading the memory cells at the voltage levels. Forexample, the statistical data can include and/or can be based on countsmeasured by calibration circuitry at the voltage levels. Optionally,such signal and noise characteristics can be measured for sub-regions inparallel to reduce the total time for measuring the signal and noisecharacteristics. The statistical data of the results generated fromreading the memory cells at the voltage levels can be used to predictwhether the decoding of the hard bit data retrieved using the optimizedread voltage is likely to require the use of soft bit data forsuccessful decoding. Thus, the transmission of the soft bit data can beperformed selectively based on the prediction.

For example, a predictive model can be generated through machinelearning to estimate or evaluate the quality of data that can beretrieved from a set of memory cells using the calibrated/optimized readvoltage(s). The predictive model can use features calculated from themeasured signal and noise characteristics of the memory cells as inputto generate a prediction. The reading and/or transmission of the softbit data can be selectively skipped based on the prediction.

After the memory device reads the group of memory cells to obtain hardbit data and/or soft bit data, an error detection and data recoverytechnique is applied to determine a set of error-free data and a countof bit errors in the hard bit data. The count of bit errors, theoptimized read voltages and/or the count differences measured todetermine the optimized read voltages can be provided to a predictivemodel configured to compute/estimate the total amount of charge loss inthe group of memory cells. The computed amount of charge loss can betracked for a subsequent coarse calibration of read voltages.

FIG. 1 illustrates an example computing system 100 that includes amemory sub-system 110 in accordance with some embodiments of the presentdisclosure. The memory sub-system 110 can include media, such as one ormore volatile memory devices (e.g., memory device 140), one or morenon-volatile memory devices (e.g., memory device 130), or a combinationof such.

A memory sub-system 110 can be a storage device, a memory module, or ahybrid of a storage device and memory module. Examples of a storagedevice include a solid-state drive (SSD), a flash drive, a universalserial bus (USB) flash drive, an embedded Multi-Media Controller (eMMC)drive, a Universal Flash Storage (UFS) drive, a secure digital (SD)card, and a hard disk drive (HDD). Examples of memory modules include adual in-line memory module (DIMM), a small outline DIMM (SO-DIMM), andvarious types of non-volatile dual in-line memory module (NVDIMM).

The computing system 100 can be a computing device such as a desktopcomputer, a laptop computer, a network server, a mobile device, avehicle (e.g., airplane, drone, train, automobile, or other conveyance),an Internet of Things (IoT) enabled device, am embedded computer (e.g.,one included in a vehicle, industrial equipment, or a networkedcommercial device), or such a computing device that includes memory anda processing device.

The computing system 100 can include a host system 120 that is coupledto one or more memory sub-systems 110. FIG. 1 illustrates one example ofa host system 120 coupled to one memory sub-system 110. As used herein,“coupled to” or “coupled with” generally refers to a connection betweencomponents, which can be an indirect communicative connection or directcommunicative connection (e.g., without intervening components), whetherwired or wireless, including connections such as electrical, optical,magnetic, etc.

The host system 120 can include a processor chipset (e.g., processingdevice 118) and a software stack executed by the processor chipset. Theprocessor chipset can include one or more cores, one or more caches, amemory controller (e.g., controller 116) (e.g., NVDIMM controller), anda storage protocol controller (e.g., PCIe controller, SATA controller).The host system 120 uses the memory sub-system 110, for example, towrite data to the memory sub-system 110 and read data from the memorysub-system 110.

The host system 120 can be coupled to the memory sub-system 110 via aphysical host interface. Examples of a physical host interface include,but are not limited to, a serial advanced technology attachment (SATA)interface, a peripheral component interconnect express (PCIe) interface,a universal serial bus (USB) interface, a Fibre Channel, a SerialAttached SCSI (SAS) interface, a double data rate (DDR) memory businterface, a Small Computer System Interface (SCSI), a dual in-linememory module (DIMM) interface (e.g., DIMM socket interface thatsupports Double Data Rate (DDR)), an Open NAND Flash Interface (ONFI), aDouble Data Rate (DDR) interface, a Low Power Double Data Rate (LPDDR)interface, or any other interface. The physical host interface can beused to transmit data between the host system 120 and the memorysub-system 110. The host system 120 can further utilize an NVM Express(NVMe) interface to access components (e.g., memory devices 130) whenthe memory sub-system 110 is coupled with the host system 120 by thePCIe interface. The physical host interface can provide an interface forpassing control, address, data, and other signals between the memorysub-system 110 and the host system 120. FIG. 1 illustrates a memorysub-system 110 as an example. In general, the host system 120 can accessmultiple memory sub-systems via a same communication connection,multiple separate communication connections, and/or a combination ofcommunication connections.

The processing device 118 of the host system 120 can be, for example, amicroprocessor, a central processing unit (CPU), a processing core of aprocessor, an execution unit, etc. In some instances, the controller 116can be referred to as a memory controller, a memory management unit,and/or an initiator. In one example, the controller 116 controls thecommunications over a bus coupled between the host system 120 and thememory sub-system 110. In general, the controller 116 can send commandsor requests to the memory sub-system 110 for desired access to memorydevices 130, 140. The controller 116 can further include interfacecircuitry to communicate with the memory sub-system 110. The interfacecircuitry can convert responses received from memory sub-system 110 intoinformation for the host system 120.

The controller 116 of the host system 120 can communicate withcontroller 115 of the memory sub-system 110 to perform operations suchas reading data, writing data, or erasing data at the memory devices130, 140 and other such operations. In some instances, the controller116 is integrated within the same package of the processing device 118.In other instances, the controller 116 is separate from the package ofthe processing device 118. The controller 116 and/or the processingdevice 118 can include hardware such as one or more integrated circuits(ICs) and/or discrete components, a buffer memory, a cache memory, or acombination thereof. The controller 116 and/or the processing device 118can be a microcontroller, special purpose logic circuitry (e.g., a fieldprogrammable gate array (FPGA), an application specific integratedcircuit (ASIC), etc.), or another suitable processor.

The memory devices 130, 140 can include any combination of the differenttypes of non-volatile memory components and/or volatile memorycomponents. The volatile memory devices (e.g., memory device 140) canbe, but are not limited to, random access memory (RAM), such as dynamicrandom access memory (DRAM) and synchronous dynamic random access memory(SDRAM).

Some examples of non-volatile memory components include a negative-and(or, NOT AND) (NAND) type flash memory and write-in-place memory, suchas three-dimensional cross-point (“3D cross-point”) memory. Across-point array of non-volatile memory can perform bit storage basedon a change of bulk resistance, in conjunction with a stackablecross-gridded data access array. Additionally, in contrast to manyflash-based memories, cross-point non-volatile memory can perform awrite in-place operation, where a non-volatile memory cell can beprogrammed without the non-volatile memory cell being previously erased.NAND type flash memory includes, for example, two-dimensional NAND (2DNAND) and three-dimensional NAND (3D NAND).

Each of the memory devices 130 can include one or more arrays of memorycells. One type of memory cell, for example, single level cells (SLC)can store one bit per cell. Other types of memory cells, such asmulti-level cells (MLCs), triple level cells (TLCs), quad-level cells(QLCs), and penta-level cells (PLCs) can store multiple bits per cell.In some embodiments, each of the memory devices 130 can include one ormore arrays of memory cells such as SLCs, MLCs, TLCs, QLCs, PLCs, or anycombination of such. In some embodiments, a particular memory device caninclude an SLC portion, an MLC portion, a TLC portion, a QLC portion,and/or a PLC portion of memory cells. The memory cells of the memorydevices 130 can be grouped as pages that can refer to a logical unit ofthe memory device used to store data. With some types of memory (e.g.,NAND), pages can be grouped to form blocks.

Although non-volatile memory devices such as 3D cross-point type andNAND type memory (e.g., 2D NAND, 3D NAND) are described, the memorydevice 130 can be based on any other type of non-volatile memory, suchas read-only memory (ROM), phase change memory (PCM), self-selectingmemory, other chalcogenide based memories, ferroelectric transistorrandom-access memory (FeTRAM), ferroelectric random access memory(FeRAM), magneto random access memory (MRAM), Spin Transfer Torque(STT)-MRAM, conductive bridging RAM (CBRAM), resistive random accessmemory (RRAM), oxide based RRAM (OxRAM), negative-or (NOR) flash memory,and electrically erasable programmable read-only memory (EEPROM).

A memory sub-system controller 115 (or controller 115 for simplicity)can communicate with the memory devices 130 to perform operations suchas reading data, writing data, or erasing data at the memory devices 130and other such operations (e.g., in response to commands scheduled on acommand bus by controller 116). The controller 115 can include hardwaresuch as one or more integrated circuits (ICs) and/or discretecomponents, a buffer memory, or a combination thereof. The hardware caninclude digital circuitry with dedicated (i.e., hard-coded) logic toperform the operations described herein. The controller 115 can be amicrocontroller, special purpose logic circuitry (e.g., a fieldprogrammable gate array (FPGA), an application specific integratedcircuit (ASIC), etc.), or another suitable processor.

The controller 115 can include a processing device 117 (processor)configured to execute instructions stored in a local memory 119. In theillustrated example, the local memory 119 of the controller 115 includesan embedded memory configured to store instructions for performingvarious processes, operations, logic flows, and routines that controloperation of the memory sub-system 110, including handlingcommunications between the memory sub-system 110 and the host system120.

In some embodiments, the local memory 119 can include memory registersstoring memory pointers, fetched data, etc. The local memory 119 canalso include read-only memory (ROM) for storing micro-code. While theexample memory sub-system 110 in FIG. 1 has been illustrated asincluding the controller 115, in another embodiment of the presentdisclosure, a memory sub-system 110 does not include a controller 115,and can instead rely upon external control (e.g., provided by anexternal host, or by a processor or controller separate from the memorysub-system).

In general, the controller 115 can receive commands or operations fromthe host system 120 and can convert the commands or operations intoinstructions or appropriate commands to achieve the desired access tothe memory devices 130. The controller 115 can be responsible for otheroperations such as wear leveling operations, garbage collectionoperations, error detection and error-correcting code (ECC) operations,encryption operations, caching operations, and address translationsbetween a logical address (e.g., logical block address (LBA), namespace)and a physical address (e.g., physical block address) that areassociated with the memory devices 130. The controller 115 can furtherinclude host interface circuitry to communicate with the host system 120via the physical host interface. The host interface circuitry canconvert the commands received from the host system into commandinstructions to access the memory devices 130 as well as convertresponses associated with the memory devices 130 into information forthe host system 120.

The memory sub-system 110 can also include additional circuitry orcomponents that are not illustrated. In some embodiments, the memorysub-system 110 can include a cache or buffer (e.g., DRAM) and addresscircuitry (e.g., a row decoder and a column decoder) that can receive anaddress from the controller 115 and decode the address to access thememory devices 130.

In some embodiments, the memory devices 130 include local mediacontrollers 150 that operate in conjunction with memory sub-systemcontroller 115 to execute operations on one or more memory cells of thememory devices 130. An external controller (e.g., memory sub-systemcontroller 115) can externally manage the memory device 130 (e.g.,perform media management operations on the memory device 130). In someembodiments, a memory device 130 is a managed memory device, which is araw memory device combined with a local controller (e.g., localcontroller 150) for media management within the same memory devicepackage. An example of a managed memory device is a managed NAND (MNAND)device.

The controller 115 and/or a memory device 130 can include a read manager113 configured to estimate an amount of charge loss in a group of memorycells using signal and noise characteristics of the group of memorycells measured during the calibration of an optimized read voltage ofthe group of memory cells and track the estimate charge loss to estimateoptimized read voltages in a subsequent calibration. In someembodiments, the controller 115 in the memory sub-system 110 includes atleast a portion of the read manager 113. In other embodiments, or incombination, the controller 116 and/or the processing device 118 in thehost system 120 includes at least a portion of the read manager 113. Forexample, the controller 115, the controller 116, and/or the processingdevice 118 can include logic circuitry implementing the read manager113. For example, the controller 115, or the processing device 118(processor) of the host system 120, can be configured to executeinstructions stored in memory for performing the operations of the readmanager 113 described herein. In some embodiments, the read manager 113is implemented in an integrated circuit chip disposed in the memorysub-system 110. In other embodiments, the read manager 113 can be partof firmware of the memory sub-system 110, an operating system of thehost system 120, a device driver, or an application, or any combinationtherein.

For example, the read manager 113 implemented in the controller 115 cantransmit a read command or a calibration command to the memory device130. In response to such a command, the read manager 113 implemented inthe memory device 130 is configured to measure signal and noisecharacteristics of a group of memory cells by reading the group ofmemory cells at a plurality of test voltages configured near anestimated location of the optimized read voltage for the group of memorycells. The test voltages can be configured to be equally spaced by asame amount of voltage gap. A result of reading the group of memorycells at a test voltage determines a bit count of memory cells in thegroup that are determined to be storing or reporting a predetermined bit(e.g., 0 or 1 corresponding to memory cells being conductive ornon-conductive at the test voltage) when the group is read at the testvoltage. A count difference can be computed from the bit counts of eachpair of adjacent test voltages. Based on the count differences measuredin the test voltage range, the read manager 113 computes an optimizedread voltage for reading hard bit data from the group of memory cellsand optionally determines a voltage window for reading correspondingsoft bit data for the decoding of the hard bit data. The memory device130 can determine a bit error rate in the hard bit data from decodingthe hard bit data. Alternatively, the memory device 130 can estimate thebit error rate in the hard bit data using the count differences. Thecount differences, the optimized read voltages, and the bit error ratecan be used as an input to a predictive model to compute the amount ofcharge loss in the group of memory cells. The amount of charge lossidentifies a nominal time of accumulating charge loss under apredetermined, nominal condition (e.g., temperature) and shifts inoptimized read voltages resulting from the amount of charge loss. Thenominal time and the shifts can be used to facilitate the estimation ofoptimized read voltages of the group of memory cells or another group ofmemory cells having a similar nominal time.

FIG. 2 illustrates an integrated circuit memory device 130 having acalibration circuit 145 configured to measure signal and noisecharacteristics according to one embodiment. For example, the memorydevices 130 in the memory sub-system 110 of FIG. 1 can be implementedusing the integrated circuit memory device 130 of FIG. 2.

The integrated circuit memory device 130 can be enclosed in a singleintegrated circuit package. The integrated circuit memory device 130includes multiple groups 131, . . . , 133 of memory cells that can beformed in one or more integrated circuit dies. A typical memory cell ina group 131, . . . , 133 can be programmed to store one or more bits ofdata.

Some of the memory cells in the integrated circuit memory device 130 canbe configured to be operated together for a particular type ofoperations. For example, memory cells on an integrated circuit die canbe organized in planes, blocks, and pages. A plane contains multipleblocks; a block contains multiple pages; and a page can have multiplestrings of memory cells. For example, an integrated circuit die can bethe smallest unit that can independently execute commands or reportstatus; identical, concurrent operations can be executed in parallel onmultiple planes in an integrated circuit die; a block can be thesmallest unit to perform an erase operation; and a page can be thesmallest unit to perform a data program operation (to write data intomemory cells). Each string has its memory cells connected to a commonbitline; and the control gates of the memory cells at the same positionsin the strings in a block or page are connected to a common wordline.Control signals can be applied to wordlines and bitlines to address theindividual memory cells.

The integrated circuit memory device 130 has a communication interface147 to receive a command having an address 135 from the controller 115of a memory sub-system 110, retrieve both hard bit data 177 and soft bitdata 173 from the memory address 135, and provide at least the hard bitdata 177 as a response to the command. An address decoder 141 of theintegrated circuit memory device 130 converts the address 135 intocontrol signals to select a group of memory cells in the integratedcircuit memory device 130; and a read/write circuit 143 of theintegrated circuit memory device 130 performs operations to determinethe hard bit data 177 and the soft bit data 173 of memory cells at theaddress 135.

The integrated circuit memory device 130 has a calibration circuit 145configured to determine measurements of signal and noise characteristics139 of memory cells in a group (e.g., 131, . . . , or 133). For example,the statistics of memory cells in a group or region that has aparticular state at one or more test voltages can be measured todetermine the signal and noise characteristics 139. Optionally, thesignal and noise characteristics 139 can be provided by the memorydevice 130 to the controller 115 of a memory sub-system 110 via thecommunication interface 147.

In at least some embodiments, the calibration circuit 145 determines theoptimized read voltage(s) of the group of memory cells based on thesignal and noise characteristics 139. In some embodiments, the signaland noise characteristics 139 are further used in the calibrationcircuit 145 to determine whether the error rate in the hard bit data 177is sufficiently high such that it is preferred to decode the hard bitdata 177 in combination with the soft bit data 173 using a sophisticateddecoder. When the use of the soft bit data 173 is predicted, based onthe prediction/classification of the error rate in the hard bit data177, the read manager 113 can transmit both the soft bit data 173 andthe hard bit data 177 to the controller 115 of the memory sub-system110.

For example, the calibration circuit 145 can measure the signal andnoise characteristics 139 by reading different responses from the memorycells in a group (e.g., 131, . . . , 133) by varying operatingparameters used to read the memory cells, such as the voltage(s) appliedduring an operation to read data from memory cells.

For example, the calibration circuit 145 can measure the signal andnoise characteristics 139 on the fly when executing a command to readthe hard bit data 177 and the soft bit data 173 from the address 135.Since the signal and noise characteristics 139 is measured as part ofthe operation to read the hard bit data 177 from the address 135, thesignal and noise characteristics 139 can be used in the read manager 113with reduced or no penalty on the latency in the execution of thecommand to read the hard bit data 177 from the address 135.

The read manager 113 of the memory device 130 is configured to use thesignal and noise characteristics 139 to determine the voltages used toread memory cells identified by the address 135 for both hard bit dataand soft bit data and to determine whether to transmit the soft bit datato the memory sub-system controller 113.

For example, the read manager 113 can use a predictive model, trainedvia machine learning, to predict the likelihood of the hard bit data 177retrieved from a group of memory cells (e.g., 131 or 133) failing a testof data integrity. The prediction can be made based on the signal andnoise characteristics 139. Before the test is made usingerror-correcting code (ECC) and/or low-density parity-check (LDPC) code,or even before the hard bit data 177 is transferred to a decoder, theread manager 113 uses the signal and noise characteristics 139 topredict the result of the test. Based on the predicted result of thetest, the read manager 113 determines whether to transmit the soft bitdata to the memory sub-system controller 113 in a response to thecommand.

For example, if the hard bit data 177 is predicted to decode using alow-power decoder that uses hard bit data 177 without using the soft bitdata 173, the read manager 113 can skip the transmission of the soft bitdata 173 to the memory sub-system controller 115; and the read manager113 provides the hard bit data 177, read from the memory cells usingoptimized read voltages calculated from the signal and noisecharacteristics 139, for decoding by the low-power decoder. For example,the low-power decoder can be implemented in the memory sub-systemcontroller 115. Alternatively, the low-power decoder can be implementedin the memory device 130; and the read manager 113 can provide theresult of the lower-power decoder to the memory sub-system controller115 as the response to the received command.

For example, if the hard bit data 177 is predicted to fail in decodingin the low-power decoder but can be decoded using a high-power decoderthat uses both hard bit data and soft bit data, the read manager 113 candecide to provide both the hard bit data 177 and the soft bit data 173for decoding by the high-power decoder. For example, the high-powerdecoder can be implemented in the controller 115. Alternatively, thehigh-power decoder can be implemented in the memory device 130.

Optionally, if the hard bit data 177 is predicted to fail in decoding indecoders available in the memory sub-system 110, the read manager 113can decide to skip transmitting the hard bit data 173 to the memorysub-system controller 115, initiate a read retry immediately, such thatwhen the memory sub-system controller 115 requests a read retry, atleast a portion of the read retry operations is performed to reduce thetime for responding to the request from the memory sub-system controller115 for a read retry. For example, during the read retry, the readmanager 133 instructs the calibration circuit 145 to perform a modifiedcalibration to obtain a new set of signal and noise characteristics 139,which can be further used to determine improved read voltages.

The data from the memory cells identified by the address (135) caninclude hard bit data 177 and soft bit data 173. The hard bit data 177is retrieved using optimized read voltages. The hard bit data 177identifies the states of the memory cells that are programmed to storedata and subsequently detected in view of changes caused by factors,such as charge loss, read disturb, cross-temperature effect (e.g., writeand read at different operating temperatures), etc. The soft bit data173 is obtained by reading the memory cells using read voltages centeredat each optimized read voltage with a predetermined offset from thecenter, optimized read voltage. The XOR of the read results at the readvoltages having the offset indicates whether the memory cells providedifferent read results at the read voltages having the offset. The softbit data 173 can include the XOR results. In some instances, one set ofXOR results is obtained based on a smaller offset; and another set ofXOR results is obtained based on a larger offset. In general, multiplesets of XOR results can be obtained for multiple offsets, where eachrespective offset is used to determine a lower read voltage and a higherread voltage such that both the lower and higher read voltages have thesame respective offset from an optimized read voltage to determine theXOR results.

FIG. 3 shows an example of measuring signal and noise characteristics139 to improve memory operations according to one embodiment.

In FIG. 3, the calibration circuit 145 applies different read voltagesV_(A), V_(B), V_(C), V_(D), and V_(E) to read the states of memory cellsin a group (e.g., 131, . . . , or 133). In general, more or less readvoltages can be used to generate the signal and noise characteristics139.

As a result of the different voltages applied during the read operation,a same memory cell in the group (e.g., 131, . . . , or 133) may showdifferent states. Thus, the counts C_(A), C_(B), C_(C), C_(D), and C_(E)of memory cells having a predetermined state at different read voltagesV_(A), V_(B), V_(C), V_(D), and V_(E) can be different in general. Thepredetermined state can be a state of having substantial current passingthrough the memory cells, or a state of having no substantial currentpassing through the memory cells. The counts C_(A), C_(B), C_(C), C_(D),and C_(E) can be referred to as bit counts.

The calibration circuit 145 can measure the bit counts by applying theread voltages V_(A), V_(B), V_(C), V_(D), and V_(E) one at a time on thegroup (e.g., 131, . . . , or 133) of memory cells.

Alternatively, the group (e.g., 131, . . . , or 133) of memory cells canbe configured as multiple subgroups; and the calibration circuit 145 canmeasure the bit counts of the subgroups in parallel by applying the readvoltages V_(A), V_(B), V_(C), V_(D), and V_(E). The bit counts of thesubgroups are considered as representative of the bit counts in theentire group (e.g., 131, . . . , or 133). Thus, the time duration ofobtaining the counts C_(A), C_(B), C_(C), C_(D), and C_(E) can bereduced.

In some embodiments, the bit counts C_(A), C_(B), C_(C), C_(D), andC_(E) are measured during the execution of a command to read the datafrom the address 135 that is mapped to one or more memory cells in thegroup (e.g., 131, . . . , or 133). Thus, the controller 115 does notneed to send a separate command to request for the signal and noisecharacteristics 139 that is based on the bit counts C_(A), C_(B), C_(C),C_(D), and C_(E).

The differences between the bit counts of the adjacent voltages areindicative of the errors in reading the states of the memory cells inthe group (e.g., 133, . . . , or 133).

For example, the count difference D_(A) is calculated from C_(A)−C_(B),which is an indication of read threshold error introduced by changingthe read voltage from V_(A) to V_(B).

Similarly, D_(B)=C_(B)−C_(C); D_(C)=C_(C)−C_(D); and D_(D)=C_(D)−C_(E).

The curve 157, obtained based on the count differences D_(A), D_(B),D_(C), and D_(D), represents the prediction of read threshold error E asa function of the read voltage. From the curve 157 (and/or the countdifferences), the optimized read voltage V_(O) can be calculated as thepoint 153 that provides the lowest read threshold error D_(MIN) on thecurve 157.

In one embodiment, the calibration circuit 145 computes the optimizedread voltage V_(O) and causes the read/write circuit 143 to read thedata from the address 135 using the optimized read voltage V_(O).

Alternatively, the calibration circuit 145 can provide, via thecommunication interface 147 to the controller 115 of the memorysub-system 110, the count differences D_(A), D_(B), D_(C), and D_(D)and/or the optimized read voltage V_(O) calculated by the calibrationcircuit 145.

FIG. 3 illustrates an example of generating a set of statistical data(e.g., bit counts and/or count differences) for reading at an optimizedread voltage V_(O). In general, a group of memory cells can beconfigured to store more than one bit in a memory cell; and multipleread voltages are used to read the data stored in the memory cells. Aset of statistical data can be similarly measured for each of the readvoltages to identify the corresponding optimized read voltage, where thetest voltages in each set of statistical data are configured in thevicinity of the expected location of the corresponding optimized readvoltage. Thus, the signal and noise characteristics 139 measured for amemory cell group (e.g., 131 or 133) can include multiple sets ofstatistical data measured for the multiple threshold voltagesrespectively.

For example, the controller 115 can instruct the memory device 130 toperform a read operation by providing an address 135 and at least oneread control parameter. For example, the read control parameter can be asuggested read voltage.

The memory device 130 can perform the read operation by determining thestates of memory cells at the address 135 at a read voltage and providethe data according to the determined states.

During the read operation, the calibration circuit 145 of the memorydevice 130 generates the signal and noise characteristics 139. The dataand the signal and noise characteristics 139 are provided from thememory device 130 to the controller 115 as a response. Alternatively,the processing of the signal and noise characteristics 139 can beperformed at least in part using logic circuitry configured in thememory device 130. For example, the processing of the signal and noisecharacteristics 139 can be implemented partially or entirely using theprocessing logic configured in the memory device 130. For example, theprocessing logic can be implemented using Complementarymetal-oxide-semiconductor (CMOS) circuitry formed under the array ofmemory cells on an integrated circuit die of the memory device 130. Forexample, the processing logic can be formed, within the integratedcircuit package of the memory device 130, on a separate integratedcircuit die that is connected to the integrated circuit die having thememory cells using Through-Silicon Vias (TSVs) and/or other connectiontechniques.

The signal and noise characteristics 139 can be determined based atleast in part on the read control parameter. For example, when the readcontrol parameter is a suggested read voltage for reading the memorycells at the address 135, the calibration circuit 145 can compute theread voltages V_(A), V_(B), V_(C), V_(D), and V_(E) that are in thevicinity of the suggested read voltage.

The signal and noise characteristics 139 can include the bit countsC_(A), C_(B), C_(C), C_(D), and C_(E). Alternatively, or in combination,the signal and noise characteristics 139 can include the countdifferences D_(A), D_(B), D_(C), and D_(D).

Optionally, the calibration circuit 145 uses one method to compute anoptimized read voltage V_(O) from the count differences D_(A), D_(B),D_(C), and D_(D); and the controller 115 uses another different methodto compute the optimized read voltage V_(O) from the signal and noisecharacteristics 139 and optionally other data that is not available tothe calibration circuit 145.

When the calibration circuit 145 can compute the optimized read voltageV_(O) from the count differences D_(A), D_(B), D_(C), and D_(D)generated during the read operation, the signal and noisecharacteristics can optionally include the optimized read voltage V_(O).Further, the memory device 130 can use the optimized read voltage V_(O)in determining the hard bit data 177 in the data from the memory cellsat the address 135. The soft bit data in the data can be obtained byreading the memory cells with read voltages that are a predeterminedoffset away from the optimized read voltage V_(O). Alternatively, thememory device 130 uses the controller-specified read voltage provided inthe read control parameter in reading the data.

The controller 115 can be configured with more processing power than thecalibration circuit 145 of the integrated circuit memory device 130.Further, the controller 115 can have other signal and noisecharacteristics applicable to the memory cells in the group (e.g., 133,. . . , or 133). Thus, in general, the controller 115 can compute a moreaccurate estimation of the optimized read voltage V_(O) (e.g., for asubsequent read operation, or for a retry of the read operation).

In general, it is not necessary for the calibration circuit 145 toprovide the signal and noise characteristics 139 in the form of adistribution of bit counts over a set of read voltages, or in the formof a distribution of count differences over a set of read voltages. Forexample, the calibration circuit 145 can provide the optimized readvoltage V_(O) calculated by the calibration circuit 145, as signal andnoise characteristics 139.

The calibration circuit 145 can be configured to generate the signal andnoise characteristics 139 (e.g., the bit counts, or bit countdifferences) as a byproduct of a read operation. The generation of thesignal and noise characteristics 139 can be implemented in theintegrated circuit memory device 130 with little or no impact on thelatency of the read operation in comparison with a typical read withoutthe generation of the signal and noise characteristics 139. Thus, thecalibration circuit 145 can determine signal and noise characteristics139 efficiently as a byproduct of performing a read operation accordingto a command from the controller 115 of the memory sub-system 110.

In general, the calculation of the optimized read voltage V_(O) can beperformed within the memory device 130, or by a controller 115 of thememory sub-system 111 that receives the signal and noise characteristics139 as part of enriched status response from the memory device 130.

The hard bit data 177 can be obtained by applying the optimized readvoltage V_(O) on the group of memory cells and determining the state ofthe memory cells while the memory cells are subjected to the optimizedread voltages V_(O).

The soft bit data 173 can be obtained by applying the read voltages 181and 182 that are offset from the optimized read voltage V_(O) with apredetermined amount. For example, the read voltage 181 is at the offset183 of the predetermined amount lower from the optimized read voltageV_(O); and the read voltage 182 is at the offset 184 of the samepredetermined amount higher from the optimized read voltage V_(O). Amemory cell subjected to the read voltage 181 can have a state that isdifferent from the memory cell subjected to the read voltage 182. Thesoft bit data 173 can include or indicate the XOR result of the dataread from the memory cell using the read voltages 181 and 182. The XORresult shows whether the memory cell subjected to the read voltage 181has the same state as being to the read voltage 182.

FIGS. 4-6 illustrate a technique to compute an optimized read voltagefrom count differences according to one embodiment. The technique ofFIGS. 4-6 simplifies the computation for calculating the optimized readvoltage V_(O) such that the computation can be implemented using reducedcomputing power and/or circuitry.

The computation illustrated in FIGS. 4-6 can be performed based on thebit counts and count differences illustrated in FIG. 3 for test voltagesV_(A), V_(B), V_(C), V_(D), and V_(E).

In FIG. 4, an operation 201 is performed to compare the two center countdifferences D_(B) and D_(C).

If D_(B) is greater than D_(C), it can be assumed that a minimal can befound on the higher half of the test voltage region between V_(C) toV_(E). Thus, operation 203 is performed to compare the lower one D_(C)of the two center bit count differences with its other neighbor D_(D).

If D_(C) is no greater than its other neighbor D_(D), D_(C) is nogreater than its neighbors D_(B) and D_(D). Thus, it can be inferredthat a minimal can be found between the test voltages V_(C) and V_(D).Based on a ratio between the differences of D_(C) from its neighborsD_(B) and D_(D), an estimate of the location of the optimized readvoltage V_(O) can be determined using a technique similar to thatillustrated in FIG. 5.

If D_(C) is greater than its other neighbor D_(D), it can be assumedthat a minimal can be in the highest test voltage interval between V_(D)and V_(E). Thus, an estimate of the location of the optimized readvoltage V_(O) can be determined using a technique similar to thatillustrated in FIG. 6, based on a ratio of count differences D_(D) andD_(C) that are closest to the test voltages V_(D) and V_(E).

Similarly, if D_(B) is no greater than D_(C), it can be assumed that aminimal can be found on the lower half of the test voltage regionbetween V_(A) to V_(C). Thus, operation 205 is performed to compare thelower one D_(B) of the two center bit count differences with its otherneighbor D_(A).

If D_(B) is less than its other neighbor D_(A), D_(B) is no greater thanits neighbors D_(A) and D_(C). Thus, it can be inferred that a minimalcan be found between the test voltages V_(B) and V_(C). Based on a ratiobetween the differences of D_(B) from its neighbors D_(A) and D_(C), anestimate of the location of the optimized read voltage V_(O) can bedetermined using a technique illustrated in FIG. 5.

If D_(B) is no less than its other neighbor D_(A), it can be assumedthat a minimal can be in the lowest test voltage interval between V_(A)and V_(B). Thus, an estimate of the location of the optimized readvoltage V_(O) can be determined using a technique illustrated in FIG. 6,based on a ratio of the count differences D_(A) and D_(B) that areclosest to the test voltages V_(A) and V_(B).

FIG. 5 illustrates a technique to estimate the location of the optimizedread voltage V_(O) when a center count difference D_(B) is no greaterthan its neighbors D_(A) and D_(C).

Since the count difference D_(B) is the difference of bit counts C_(B)and C_(C) at test voltages V_(B) and V_(C), the location of theoptimized read voltage V_(O) is estimated to be within the voltageinterval or gap between V_(B) and V_(C).

When the increases from the center count difference D_(B) to itsneighbors D_(A) and D_(C) are substantially equal to each other, theoptimized read voltage V_(O) is estimated at the midpoint between V_(B)and V_(C).

The ratio between the increases from the center count difference D_(B)to its neighbors D_(A) and D_(C) can be mapped in a logarithmic scale toa line scale of division between the test voltages V_(B) and V_(C).

For example, the ratio (D_(A)−D_(B))/(D_(C)−D_(B)) of 1 is mapped to alocation of the optimized read voltage at the midpoint between the testvoltages V_(B) and V_(C).

The ratio (D_(A)−D_(B))/(D_(C)−D_(B)) of ½ is mapped to a location ofthe optimized read voltage at the midpoint between the test voltagesV_(B) and V_(C) with an offset of an fixed increment towards V_(B). Forexample, the increment can be one tenth of the voltage gap between V_(B)and V_(C).

Similarly, the ratio (D_(A)−D_(B))/(D_(C)−D_(B)) of ¼, ⅛, or 1/16 ismapped to a location of the optimized read voltage at the midpointbetween the test voltages V_(B) and V_(C) with an offset of two, three,or four increments towards V_(B). A ratio (D_(A)−D_(B))/(D_(C)−D_(B))smaller than 1/16 can be mapped to a location of the optimized readvoltage at V_(B).

Similarly, the ratio (D_(C)−D_(B))/(D_(A)−D_(B)) of ½, ¼, ⅛, or 1/16 ismapped to a location of the optimized read voltage at the midpointbetween the test voltages V_(B) and V_(C) with an offset of one, two,three, or four increments towards V_(C). A ratio(D_(C)−D_(B))/(D_(A)−D_(B)) smaller than 1/16 can be mapped to alocation of the optimized read voltage at V_(C).

The technique of FIG. 5 can be implemented via setting a coarseestimation of the optimized read voltage at V_(B) (or V_(C)) andadjusting the coarse estimation through applying the increment accordingto comparison of the increase (D_(A)−D_(B)) of the count differenceD_(B) to the count difference D_(A) with fractions or multiples of theincrease (D_(C)−D_(B)) of the count difference D_(B) to the countdifference D_(C). The fractions or multiples of the increase(D_(C)−D_(B)) in a logarithmic scale can be computed through iterativedivision or multiplication by two, which can be implemented efficientlythrough bit-wise left shift or right shift operations.

For example, the initial estimate of the optimized voltage V_(O) can beset at the test voltage V_(B). The increase (D_(A)−D_(B)) can becompared with (D_(C)−D_(B))/16, which can be computed through shiftingthe bits of (D_(C)−D_(B)). If (D_(A)−D_(B)) is greater than(D_(C)−D_(B))/16, the increment of one tenth of the gap between V_(B)and V_(C) can be added to the estimate of the optimized voltage V_(O).Subsequently, (D_(A)−D_(B)) is compared to (D_(C)−D_(B))/8, which can becalculated by shifting the bits of (D_(C)−D_(B))/16. If (D_(A)−D_(B)) isgreater than (D_(C)−D_(B))/8, the same increment of one tenth of the gapbetween V_(B) and V_(C) is further added to the estimation of theoptimized voltage V_(O). Similarly, (D_(A)−D_(B)) is compared to(D_(C)−D_(B))/4, (D_(C)−D_(B))/2, (D_(C)−D_(B)), (D_(C)−D_(B))*2,(D_(C)−D_(B))*4, (D_(C)−D_(B))*8, and (D_(C)−D_(B))*16 one afteranother. If (D_(A)−D_(B)) is greater than any of these scaled versionsof (D_(C)−D_(B)) in a comparison, the same increment is added to theestimate. After the series of comparisons, the resulting estimate can beused as the optimized voltage V_(O).

FIG. 6 illustrates a technique to estimate the location of the optimizedread voltage V_(O) when a side count difference D_(A) is smaller thanits next two count differences D_(B) and D_(C), but one of its neighborshas not been measured (e.g., a count difference between the test voltageV_(A) and a further test voltage that is lower than V_(A)).

Since the count difference D_(A) is the lowest among count differencesD_(A), D_(B) and D_(C), the optimized voltage V_(O) is estimated to bein the test voltage interval gap corresponding to the count differenceD_(A). Since the count difference D_(A) is the difference of bit countsC_(A) and C_(B) at test voltages V_(A) and V_(B), the location of theoptimized read voltage V_(O) is estimated to be within the voltageinterval or gap between V_(A) and V_(B).

In FIG. 6, the location of the optimized read voltage V_(O) within thevoltage interval or gap between V_(A) and V_(B) is based on a ratio ofthe count differences D_(A) and D_(B). The ratio D_(A)/D_(B) in alogarithmic scale is mapped to the linear distribution of the optimizedread voltage V_(O) between V_(A) and V_(B).

For example, the voltage interval or gap between V_(A) and V_(B) can bedivided into five equal increments. The initial estimate of theoptimized voltage V_(O) can be set at the test voltage V_(B). The countdifference D_(A) can be compared to scaled versions of the countdifference D_(B) sequentially, such as D_(B), D_(B)/2, and D_(B)/4. Ifthe count difference D_(A) is smaller than any of the scaled versions ofthe count difference D_(B) in a comparison, the estimate is reduced bythe increment for moving towards the test voltage V_(A).

FIGS. 4-6 illustrate examples of techniques that can be used tocalculate the optimized read voltage V_(O) when the optimized readvoltage V_(O) is seen to be within the test voltage range V_(A) toV_(E). After the optimized read voltage V_(O) is calculated, the voltagewindow (e.g., between voltage 181 and 182 illustrated in FIG. 3) forreading soft bit data 173 can be calculated based on a predeterminedsize for the offsets (e.g., 183 and 184) from the optimized read voltageV_(O).

However, in some instances, soft bit data 173 read using an alternativevoltage window can provide improved results in decoding the hard bitdata 177. For example, in some instances, a voltage window having a sizethat is larger (or smaller) than the standard gap (e.g., 100 mV)pre-defined by the predetermined offsets (e.g., 183 and 183) can beoptimal, or better than the standard gap, in generating soft bit data173 for the decoding of the hard bit data 177. In other instances, avoltage window not centered at the calculated/optimized/calibrated readvoltage V_(O), and thus asymmetric about thecalculated/optimized/calibrated read voltage V_(O), can be optimal, orbetter than the symmetric voltage window, in generating soft bit data173 for the decoding of the hard bit data 177, as further discussedbelow.

FIG. 7 shows a technique to determine charge loss using signal and noisecharacteristics of memory cells collected during calibration of theoptimized read voltages of the memory cells according to one embodiment.

When a group of memory cells are configured to each store multiple bitsof data, the group of memory cells can be read at a plurality of voltagelevels to determine the states of the group of memory cells at thedifferent levels and thus the bits of data stored in each memory cell.An optimized read voltage at each level can be used to determine thestate of a memory cell at the level to best determine the bits of datastored in the memory cell.

In FIG. 7, read voltages 221 from a coarse calibration are used as theinitial estimates of the optimized read voltages 223 at the differentlevels. Each of the optimized read voltage can be calibrated viameasuring signal and noise characteristics 139 in a test voltage rangeV_(A) to V_(E) centered at a corresponding read voltage 221 from thecoarse calibration.

The signal and noise characteristics 139 measured for the determinationof an optimized read voltage 223 at a level can include countdifferences D_(A) to D_(D) on a test voltage range V_(A) to V_(E), asillustrated in FIG. 3. The read voltage 221 at the level from the coarsecalibration 221 can be used as test voltage V_(C). An optimized readvoltage V_(O) can be determined from the count differences D_(A) toD_(D) using the techniques illustrated in FIGS. 3-6. Thus, optimizedread voltages 223 can be determined for the different read levels fromthe read voltages 221 from the coarse calibration and the signal andnoise characteristics 139 measured in test voltage ranges configuredaccording to the read voltages 221 from the coarse calibration.

The optimized read voltages 223 can be used to read memory data 225,such as the hard bit data 177 at the optimized read voltages 223 andoptionally soft bit data 173 at voltages having an offset from eachoptimized read voltages (e.g., 181 and 182).

The memory data 225 is decoded via error detection and recovery 175 togenerate error-free data that have been previously written into thememory cells. The operation of error detection and recovery 175identifies a count of the bit errors 227 in the hard bit data 177. Thebit errors 227 identifies a bit error rate in the memory data 225.

A predictive model 211 receives the signal and noise characteristics139, the optimized read voltages 223 and the count of the bit errors 227as input to output an amount of charge loss 213. The amount of chargeloss 213 can be used to update a time pool 217 and/or shifts 215 inoptimized read voltages caused by charge loss.

In some implementations, the count differences D_(A) to D_(D) measuredin the test voltage range V_(A) to V_(E) for the calibration of the readvoltage V_(O) at the highest level and the highest optimized readvoltage V_(O) are used in the predictive model 211. In otherimplementations, signal and noise characteristics 139 measured for otherlevels of read voltages and the corresponding optimized read voltagesare also used in the predictive model 211.

In some implementations, the count of actual bit errors 227 determinedin the error detection and recovery 175 is used in predictive model 211as input. In other implementations, the count of bit errors 227 isestimated/predicted using the signal and noise characteristics 139(e.g., count differences D_(A) to D_(D)).

Optionally, the amount of charge loss 213 can be represented by anominal length of time during which the amount of charge loss 213 canaccumulate in a group of memory cells under a predetermined condition(e.g., the memory cells being at a predetermined temperature during thenominal length of time). Thus, the amount of charge loss 213 identifiesa nominal time of writing data into the group of memory cells. Thenominal write time is the nominal length of time before the measuring ofthe signal and noise characteristics 139 and can be different from theactual time of writing data into the group of memory cells, because thetemperature history between the actual time of write data into the groupof memory cell to the measuring of the signal and noise characteristics139 is generally different from the predetermined temperature.

The group of memory cells having the amount of charge loss 213 can beplaced in a time pool 217 having the nominal write time. Another groupof memory cell having the same amount of charge loss 213 (or similaramount of charge loss within a threshold in differences) can be assignedto the same time pool 217.

For example, when the difference between the actual times of write datainto two groups of memory cells is within a threshold, the two groups ofmemory cells can be assigned to the same time pool 217. When the twogroups of memory cells are read in times within a threshold, the amountof charge loss 213 can be computed using the predictive model 211 foreach of the two groups to determine their respective nominal writetimes. When the nominal write times are substantially different (e.g.,more than a threshold), one of the groups can be moved to another timepool that has a nominal write time that is close to the nominal writetime of the group being moved. Thus, groups of memory cells can betracked in time pools based on their similar amounts of charge loss andtheir similar nominal write times.

The time pool 217 has a nominal write time slot 219 that corresponds toamounts of charge loss (e.g., 213) that are within a predeterminedrange. Groups of memory cells having their nominal write times fallingwithin the slot 219 are assigned to the time pool 217 for the trackingof their charge loss over time. Each group of memory cells in the timepool 217 approximately has the amount of charge loss 213 at the time 229of the amount 213 of charge loss being determined from the measuredsignal and noise characteristics 139. A most recently read/calibratedgroup in the time pool 217 can have its amount 213 of charge losscalculated using the predictive model to update the time pool 217.

In general, after the time 229 of charge loss amount 213 is determined,memory cells in the time pool 217 can have further charge loss. Theadditional charge loss can be estimated based on the temperature historybetween the time 229 and the current time instant. The estimate can beused to update the amount 213 and the time 229 of the time pool 217 suchthat the shifts 215 in optimized read voltages for the updated amountcan be used as a coarse calibration of the read voltages of the timepool 217. The nominal write time slot 219 can also be updated to accountfor the deviation of the temperature history from the predeterminedtemperature.

Alternatively, or in combination, the amount 213 can be updated usingthe predictive model 211 whenever a group in the time pool 217 has itsthe signal and noise characteristics 139 measured. The slot 219 can beupdated according to the amount of charge loss 213 calculated by thepredictive model 211; and the time 229 can be updated according to thetime of measuring the signal and noise characteristics 139.

Computing the amount 213 of charge loss using the predictive model 211allows the memory sub-system 110 to track and update time pools (e.g.,217) without a temperature history.

For example, after the memory sub-system 110 is powered off for a periodof time and then powered on, the memory sub-system 110 can identify atime pool 217 having a recorded time 229 of charge loss amount 213 thatis close to the time of powering off. The memory device 130 then reads agroup of memory cells in the time pool 217. The amount of charge loss213 can be computed for the group calibrated using the signal and noisecharacteristics 139 to update the nominal write time slot 219 and thetime 229 of charge loss amount. Further, the updates to the nominalwrite time slot 219 and/or the time 229 of charge loss amount can beused to calculate the nominal time duration of powering off and/or theeffect of temperate deviation from the predetermined temperature, suchthat other time pools can also be updated accordingly to account for theduration of powering off and/or the effect of temperate deviation fromthe predetermined temperature, without requiring the temperature historyduring the powering off period.

FIG. 8 shows a method to track charge loss according to one embodiment.The method of FIG. 8 can be performed by processing logic that caninclude hardware (e.g., processing device, circuitry, dedicated logic,programmable logic, microcode, hardware of a device, integrated circuit,etc.), software/firmware (e.g., instructions run or executed on aprocessing device), or a combination thereof. In some embodiments, themethod of FIG. 8 is performed at least in part by the controller 115 ofFIG. 1, or processing logic in the memory device 130 of FIG. 2. Althoughshown in a particular sequence or order, unless otherwise specified, theorder of the processes can be modified. Thus, the illustratedembodiments should be understood only as examples, and the illustratedprocesses can be performed in a different order, and some processes canbe performed in parallel. Additionally, one or more processes can beomitted in various embodiments. Thus, not all processes are required inevery embodiment. Other process flows are possible.

For example, the method of FIG. 8 can be implemented in a computingsystem of FIG. 1 with a memory device of FIG. 2 and signal noisecharacteristics illustrated in FIG. 3 with some of the operationsillustrated in FIGS. 4-7.

At block 301, a memory device 130 measures signal and noisecharacteristics 139 of a group of memory cells (e.g., 131 or 133) in thememory device 130.

For example, the memory device 130 can ramp up the read voltage appliedon the group of memory cells to V_(C) to count the number of memorycells that output one (or zero) as the bit count C_(C) at V_(C). Then,the memory device 130 can boost modulate the read voltages of foursub-groups in the group to V_(A), V_(B), V_(D) and V_(E) respectively inparallel to count the number of memory cells that output one (or zero)at V_(A), V_(B), V_(D) and V_(E) respective. The counts of thesub-groups can be scaled according to the population ratio(s) betweensub-groups and the entire group to determine the bit counts C_(A),C_(B), C_(D) and C_(E) respectively.

Alternatively, the memory device 130 can read the group of memory cellsat V_(A) to V_(E) sequentially to determine the bit counts C_(A) toC_(E).

At block 303, the memory device 130 calculates an optimized read voltageV_(O) of the group of memory cells (e.g., 131 or 133) from the signaland noise characteristics 139.

For example, the signal and noise characteristics 139 can include countdifferences D_(A) to D_(D); and the optimized read voltage V_(O) can bedetermined using the techniques illustrated in FIGS. 3-7.

At block 305, the memory device 130 and/or the memory sub-system 110 canidentify a bit error rate in data 225 read from the group of memorycells (e.g., 131 or 133) using the optimized read voltage V_(O).

For example, the bit error rate can be based on a count of bit errors227 actually identified from error detection and recovery 175 of thememory data 225 read using the optimized read voltages 223.

Alternatively, the bit error rate can be estimated based on the signaland noise characteristics 139 without actually reading the memory data225 and/or without performing the error detection and recovery 175.

At block 307, the memory device 130 and/or the memory sub-system 110 candetermine an amount 213 of charge loss in the group of memory cells(e.g., 131 or 133) based at least in part on the signal and noisecharacteristics 139 and the bit error rate.

For example, the amount 211 of charge loss in the group of memory cellscan be determined further based on the optimized read voltage V_(O) ofthe group of memory cells (e.g., 131 or 133).

The group of memory cells (e.g., 131 or 133) can be configured to storemultiple bits per memory cell and has a plurality of voltage levels forreading. The signal and noise characteristics 139 used in the predictivemodel 211 can include the count differences D_(A) to D_(D) for thecalculation of the optimized read voltage V_(O) at the highest levelamong the plurality voltage levels. Optionally, count differencesassociated with one or more optimized read voltages at other levels canalso be used in a predictive model 211 as input to determine the amount213 of charge loss.

For example, the predictive model 211 can be trained through machinelearning to calculate the amount of charge loss 213. Alternatively, thepredictive model 211 can be established through a statistical analysis,curve fitting, tree-based classification, etc.

At block 309, the memory device 130 and/or the memory sub-system 110 cantrack changes (e.g., 215) in optimized read voltages of memory cells inthe memory device 130 based on the amount 213 of charge loss.

For example, a time pool 217 can be used to identify a set of memorycell groups having similar amounts of charge loss that are within apredetermined range. Based on the amount 213 of charge loss in the groupof memory cells (e.g., 131 or 133), the memory device 130 and/or thememory sub-system 110 can assign the group (e.g., 131 or 133) to a timepool 217 and/or update an attribute of the time pool 217 that includesthe group of memory cells (e.g., 131 or 133).

For example, the attribute can be configured to identify a range ofcharge loss amount at the time 229 of the measuring of the signal andnoise characteristics 139 of the group of memory cells (e.g., 131 or133).

For example, the attribute can be configured to identify shifts 215 ofoptimized read voltages 223 of memory cells in the pool at the time 229of the measuring of the signal and noise characteristics 139 of thegroup of memory cells (e.g., 131 or 133).

For example, the attribute can be configured to identify a nominal timeof writing data in the group of memory cells (e.g., 131 or 133). Under apredefined condition (e.g., with a predefined temperature of the memorycells), the amount 213 of charge loss can accumulate in the time periodfrom the nominal time to the time of the measuring of the signal andnoise characteristics 139 of the group of memory cells (e.g., 131 or133). Since the actually temperature history of the group of memorycells (e.g., 131 and 133) can be different from the predefinedtemperature, the nominal time of writing data in the group of memorycells (e.g., 131 or 133) can be different from the actual time ofwriting data into the group of memory cells (e.g., 131 or 133).

For example, the memory device 130 and/or the memory sub-system 110 candetermine a nominal time duration between the previous update of anattribute of the time pool 217 and the current update of the attribute.The nominal time duration causes, under the predefined condition, chargeloss to increase in the group (e.g., 131 or 133) to the amount 213 ofcharge loss determined based on the signal and noise characteristics 139of the group of memory cells (e.g., 131 or 133). The nominal timeduration can be different from the actual time duration that causescharge loss to increase to the amount 213 between the previous updateand the current update.

The nominal time duration can be used to update the attributes of aplurality of pools that have not been updated during the time period.For examples, when the memory device is powered off during at least aportion of the period between the previous update and the currentupdate, the charge loss that occurs during this period is not trackedand/or updated for the time pools. The nominal time duration determinedfor the period can be used to update different time pools that areconfigured to identify different sets of memory cell groups havingdifferent ranges of charge loss amount respectively.

Similarly, if the memory device 130 and/or the memory sub-system 110 isidling in a period of time where the time pools are not updated, anominal time duration can be similarly determined to account for thecharge loss accumulated during the period of idle time.

Similarly, when some time pools are not updated for a period of time dueto the lack of read/calibration activities during the period of time forthe memory cell groups in the pools, such time pools can be similarlyupdated using one or more nominal time lengths, or use a nominal timelength determined for a time group that is updated for a similar periodof time.

A non-transitory computer storage medium can be used to storeinstructions of the firmware of a memory sub-system (e.g., 110). Whenthe instructions are executed by the controller 115 and/or theprocessing device 117, the instructions cause the controller 115, theprocessing device 117, and/or a separate hardware module to perform themethods discussed above.

FIG. 9 illustrates an example machine of a computer system 400 withinwhich a set of instructions, for causing the machine to perform any oneor more of the methodologies discussed herein, can be executed. In someembodiments, the computer system 400 can correspond to a host system(e.g., the host system 120 of FIG. 1) that includes, is coupled to, orutilizes a memory sub-system (e.g., the memory sub-system 110 of FIG. 1)or can be used to perform the operations of a read manager 113 (e.g., toexecute instructions to perform operations corresponding to the readmanager 113 described with reference to FIGS. 1-8). In alternativeembodiments, the machine can be connected (e.g., networked) to othermachines in a LAN, an intranet, an extranet, and/or the Internet. Themachine can operate in the capacity of a server or a client machine inclient-server network environment, as a peer machine in a peer-to-peer(or distributed) network environment, or as a server or a client machinein a cloud computing infrastructure or environment.

The machine can be a personal computer (PC), a tablet PC, a set-top box(STB), a Personal Digital Assistant (PDA), a cellular telephone, a webappliance, a server, a network router, a switch or bridge, or anymachine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while a single machine is illustrated, the term “machine” shall also betaken to include any collection of machines that individually or jointlyexecute a set (or multiple sets) of instructions to perform any one ormore of the methodologies discussed herein.

The example computer system 400 includes a processing device 402, a mainmemory 404 (e.g., read-only memory (ROM), flash memory, dynamic randomaccess memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM(RDRAM), static random access memory (SRAM), etc.), and a data storagesystem 418, which communicate with each other via a bus 430 (which caninclude multiple buses).

Processing device 402 represents one or more general-purpose processingdevices such as a microprocessor, a central processing unit, or thelike. More particularly, the processing device can be a complexinstruction set computing (CISC) microprocessor, reduced instruction setcomputing (RISC) microprocessor, very long instruction word (VLIW)microprocessor, or a processor implementing other instruction sets, orprocessors implementing a combination of instruction sets. Processingdevice 402 can also be one or more special-purpose processing devicessuch as an application specific integrated circuit (ASIC), a fieldprogrammable gate array (FPGA), a digital signal processor (DSP),network processor, or the like. The processing device 402 is configuredto execute instructions 426 for performing the operations and stepsdiscussed herein. The computer system 400 can further include a networkinterface device 408 to communicate over the network 420.

The data storage system 418 can include a machine-readable storagemedium 424 (also known as a computer-readable medium) on which is storedone or more sets of instructions 426 or software embodying any one ormore of the methodologies or functions described herein. Theinstructions 426 can also reside, completely or at least partially,within the main memory 404 and/or within the processing device 402during execution thereof by the computer system 400, the main memory 404and the processing device 402 also constituting machine-readable storagemedia. The machine-readable storage medium 424, data storage system 418,and/or main memory 404 can correspond to the memory sub-system 110 ofFIG. 1.

In one embodiment, the instructions 426 include instructions toimplement functionality corresponding to a read manager 113 (e.g., theread manager 113 described with reference to FIGS. 1-8). While themachine-readable storage medium 424 is shown in an example embodiment tobe a single medium, the term “machine-readable storage medium” should betaken to include a single medium or multiple media that store the one ormore sets of instructions. The term “machine-readable storage medium”shall also be taken to include any medium that is capable of storing orencoding a set of instructions for execution by the machine and thatcause the machine to perform any one or more of the methodologies of thepresent disclosure. The term “machine-readable storage medium” shallaccordingly be taken to include, but not be limited to, solid-statememories, optical media, and magnetic media.

Some portions of the preceding detailed descriptions have been presentedin terms of algorithms and symbolic representations of operations ondata bits within a computer memory. These algorithmic descriptions andrepresentations are the ways used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. The presentdisclosure can refer to the action and processes of a computer system,or similar electronic computing device, that manipulates and transformsdata represented as physical (electronic) quantities within the computersystem's registers and memories into other data similarly represented asphysical quantities within the computer system memories or registers orother such information storage systems.

The present disclosure also relates to an apparatus for performing theoperations herein. This apparatus can be specially constructed for theintended purposes, or it can include a general purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program can be stored in a computerreadable storage medium, such as, but not limited to, any type of diskincluding floppy disks, optical disks, CD-ROMs, and magnetic-opticaldisks, read-only memories (ROMs), random access memories (RAMs), EPROMs,EEPROMs, magnetic or optical cards, or any type of media suitable forstoring electronic instructions, each coupled to a computer system bus.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems can be used with programs in accordance with the teachingsherein, or it can prove convenient to construct a more specializedapparatus to perform the method. The structure for a variety of thesesystems will appear as set forth in the description below. In addition,the present disclosure is not described with reference to any particularprogramming language. It will be appreciated that a variety ofprogramming languages can be used to implement the teachings of thedisclosure as described herein.

The present disclosure can be provided as a computer program product, orsoftware, that can include a machine-readable medium having storedthereon instructions, which can be used to program a computer system (orother electronic devices) to perform a process according to the presentdisclosure. A machine-readable medium includes any mechanism for storinginformation in a form readable by a machine (e.g., a computer). In someembodiments, a machine-readable (e.g., computer-readable) mediumincludes a machine (e.g., a computer) readable storage medium such as aread only memory (“ROM”), random access memory (“RAM”), magnetic diskstorage media, optical storage media, flash memory components, etc.

In this description, various functions and operations are described asbeing performed by or caused by computer instructions to simplifydescription. However, those skilled in the art will recognize what ismeant by such expressions is that the functions result from execution ofthe computer instructions by one or more controllers or processors, suchas a microprocessor. Alternatively, or in combination, the functions andoperations can be implemented using special purpose circuitry, with orwithout software instructions, such as using Application-SpecificIntegrated Circuit (ASIC) or Field-Programmable Gate Array (FPGA).Embodiments can be implemented using hardwired circuitry withoutsoftware instructions, or in combination with software instructions.Thus, the techniques are limited neither to any specific combination ofhardware circuitry and software, nor to any particular source for theinstructions executed by the data processing system.

In the foregoing specification, embodiments of the disclosure have beendescribed with reference to specific example embodiments thereof. Itwill be evident that various modifications can be made thereto withoutdeparting from the broader spirit and scope of embodiments of thedisclosure as set forth in the following claims. The specification anddrawings are, accordingly, to be regarded in an illustrative senserather than a restrictive sense.

1. A method, comprising: measuring signal and noise characteristics of a group of memory cells in a memory device; calculating an optimized read voltage of the group of memory cells from the signal and noise characteristics; identifying a bit error rate in data read from the group of memory cells using the optimized read voltage; determining an amount of charge loss in the group of memory cells based at least in part on the signal and noise characteristics and the bit error rate; and tracking changes in optimized read voltages of memory cells in the memory device based on the amount of charge loss.
 2. The method of claim 1, wherein the signal and noise characteristics identify a distribution of count difference over the plurality of test voltages according to bit counts on the test voltages respectively; wherein each respective bit count at a test voltage identifies a number of memory cells in the group that, when read at the test voltage, provide a predetermined bit value; and wherein each respective count difference between two adjacent test voltages represents a difference between bit counts at the adjacent test voltages respectively.
 3. The method of claim 2, wherein the optimized read voltage is calculated from the distribution of count difference over the plurality of test voltages.
 4. The method of claim 3, wherein the amount of charge loss in the group of memory cells is determined further based on the optimized read voltage of the group of memory cells.
 5. The method of claim 4, wherein the amount of charge loss in the group of memory cells is determined using a predictive model trained using a machine learning technique.
 6. The method of claim 4, further comprising: decoding the data, read from the group of memory cells according to the optimized read voltage, using an error detection and recovery technique; wherein the bit error rate is identified from a count of bit errors identified during the decoding.
 7. The method of claim 4, wherein the group of memory cells is configured to store multiple bits per memory cell and has a plurality of voltage levels for reading; and the optimized read voltage is at a highest level among the plurality of voltage levels.
 8. The method of claim 4, wherein the tracking of changes in optimized read voltages of memory cells in the memory device includes: assigning the group to a pool based on the amount of charge loss in the group of memory cells; wherein the pool is configured to identify a set of memory cell groups having amounts of charge loss within a predetermined range.
 9. The method of claim 4, wherein the tracking of changes in optimized read voltages of memory cells in the memory device includes: updating, based on the amount of charge loss in the group of memory cells, an attribute of a pool that includes the group of memory cells; wherein the pool is configured to identify a set of memory cell groups having amounts of charge loss within a predetermined range.
 10. The method of claim 9, wherein the attribute identifies a range of charge loss amount at a time of the measuring of the signal and noise characteristics of the group of memory cells.
 11. The method of claim 9, wherein the attribute identifies shifts of optimized read voltages of memory cells in the pool at a time of the measuring of the signal and noise characteristics of the group of memory cells.
 12. The method of claim 9, wherein the attribute identifies a nominal time of writing data in the group of memory cells where under a predefined condition the amount of charge loss accumulates from the nominal time to a time of the measuring of the signal and noise characteristics of the group of memory cells.
 13. The method of claim 12, further comprising: determining a nominal time duration between a previous update of the attribute and a current update of the attribute, wherein the nominal time duration causes charge loss to increase in the group under the predefined condition to the amount of charge loss determined based on the signal and noise characteristics of the group of memory cells; and updating attributes of a plurality of pools based on the nominal time duration; wherein the pools are configured to identify different sets of memory cell groups having different ranges of charge loss amount respectively.
 14. The method of claim 13, wherein the memory device is powered off during at least a portion between the previous update and the current update.
 15. A memory device, comprising: an integrated circuit package enclosing the memory device; and a plurality of groups of memory cells formed on at least one integrated circuit die; wherein the memory device is configured to: measure signal and noise characteristics of a group of memory cells in the memory device; calculate an optimized read voltage of the group of memory cells from the signal and noise characteristics; identify a bit error rate in data read from the group of memory cells using the optimized read voltage; determine an amount of charge loss in the group of memory cells based at least in part on the signal and noise characteristics and the bit error rate; and track changes in optimized read voltages of memory cells in the memory device based on the amount of charge loss.
 16. The memory device of claim 15, wherein the signal and noise characteristics identify a distribution of count difference over the plurality of test voltages according to bit counts on the test voltages respectively; wherein each respective bit count at a test voltage identifies a number of memory cells in the group that, when read at the test voltage, provide a predetermined bit value; and wherein each respective count difference between two adjacent test voltages represents a difference between bit counts at the adjacent test voltages respectively.
 17. The memory device of claim 16, wherein the optimized read voltage and the bit error rate are both calculated based on the distribution of count difference over the plurality of test voltages.
 18. The memory device of claim 16, wherein the memory device is configured to update, based on the amount of charge loss in the group of memory cells, an attribute of a pool configured to identify a set of memory cell groups having charge loss amounts that are within a predetermined range.
 19. A memory sub-system, comprising: a processing device; and at least one memory device, the memory device having a group of memory cells formed on an integrated circuit die; wherein the processing device is configured to transmit, to the memory device, a read command with an address identifying the group of memory cells; wherein in response to the read command, the memory device is configured to: measure signal and noise characteristics of the group of memory cells in the memory device; and calculate an optimized read voltage of the group of memory cells from the signal and noise characteristics; wherein the memory sub-system is configured to: determine an amount of charge loss in the group of memory cells based at least in part on the signal and noise characteristics; and track changes in optimized read voltages of memory cells in the memory device based on the amount of charge loss; wherein the signal and noise characteristics identifies a distribution of count difference over the plurality of test voltages according to bit counts on the test voltages respectively; wherein each respective bit count at a test voltage identifies a number of memory cells in the group that, when read at the test voltage, provide a predetermined bit value; and wherein each respective count difference between two adjacent test voltages represents a difference between bit counts at the adjacent test voltages respectively.
 20. The memory sub-system of claim 19, further configured to update, based on the amount of charge loss in the group of memory cells, an attribute of a pool configured to identify a set of memory cell groups having charge loss amounts that are within a predetermined range. 